A Study on the Application of AI-Driven Personalized Music Learning in Cultivating Musical Creativity in Higher Education

Abstract

With the rapid development of artificial intelligence, AI-driven personalized learning has begun to reshape the teaching and learning methods of music courses in higher education. This study explores the role of artificial intelligence in promoting the development of college students’ musical creativity through a combination of theoretical analysis, classroom observation, and teaching practice. Research findings show that in the early stage of work creation, AI can provide music materials with distinct styles and multiple generated versions, helping students break through creative bottlenecks and stimulate divergent thinking. During the process of secondary creation, the initial materials provided by AI will make students pay more attention to whether the structure is clear, whether the musical phrases are coherent, and whether the musical development is reasonable, and make their structural awareness stronger. In addition, AI can provide results quickly and switch to multiple styles, which enables students to be exposed to more types of music, making them less nervous when creating and more willing to get involved. Based on the above findings, this study proposes relevant teaching strategies: constructing an AI-assisted exploratory learning cycle, strengthening secondary creation tasks, establishing a process-oriented assessment mechanism, and enhancing teachers’ AI literacy. Research shows that AI can help students enter the creative state more quickly, understand the structure of music, and expose them to more diverse styles. However, whether these effects can truly take effect still depends on how teachers guide and whether the course design is reasonable. This study also provides valuable references for universities on how to apply AI in music teaching centered on creativity.